DETAILED ACTION
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 6-8, 12-17, 36 are objected to because of the following informalities:
In Claim 1, line 3, end of the preamble, “the system comprising” should end with colon “:”.
Claim 1 recites in line 9, “a real-time simulation translation” and claims 13, 25, 26, 33, 34 recite “simulation translation”. It’s not clear whether the “a real-time simulation translation” in claim 1 and “simulation translation” in claim 13, 25, 26, 33, 34 are same. For examination purpose all the modules are considered same as “simulation translation”.
Claim 12 is listed in the claim list, but there is no limitation listed. For examination purpose claim 12 is considered as cancelled.
Claim 36 recites in line 1, “The method of claim 28, wherein the device..” , whereas claim 28 is a system claim. Also, claim 36 is similar to system claim 27. For examination purpose claim 36 is considered as method claim, depending on claim 35.
Appropriate corrections are required.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show any label with the Figs. 1, 2, 3, as described in the specification. Figs. 1, 2, 3 only listed different boxes with numbers, but there is no associated labels. Also, as per specification, Fig. 3 is a flowchart of a method, but Fig. 3 is just some stacked numbered boxes, without any connection between them. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
35 U.S.C. 112(f) Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a nonstructural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for'' (e.g., "means for'') or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word "means" (or "step") in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting
sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word "means" (or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder ( “configured to”) that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“ sound sensor module” in claim 1,
“ sound processing module” in claim 1,
“ storage module” in claim 1,
“ simulation module” in claims 1, 24,
“ segregation module” in claim 2,
“ transcription module” in claim 6,
“ transcript module” in claim 7,8,
“ translation module” in claim 17,
“ conversion module ” in claims 18, 20,
“ accuracy module” in claim 21,
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being
interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1- 34 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 1 recites in line 12, “ an accurate transcript” and claims 13, 23, 24, 25 recite “accuracy transcript”. It’s not clear whether the “accurate transcript” in claim 1 and “accuracy transcript” in claim 13, 23, 24, 25 are same. Since specification didn’t mention any “ accurate transcript”, for examination purpose all the modules are considered as same as “accuracy transcript” as specified in specification.
Claims 6, 14, 15, 16, 17 recite “transcription module” and claims 7, 8 recite “transcript module”. It’s not clear whether the “transcription module” in claims 6, 14, 15, 16, 17 and “transcript module” in claim 7, 8 are same. Since specification didn’t mention any “transcript module”, for examination purpose all the modules are considered as same as “transcription module” as specified in specification.
Claim 13 is not depending on any claim. It’s not clear whether claim 13 is a dependent claim or an independent claim. Based on the subject matter recited in claim 13, examiner has interpreted that claim 13 is a system claim and depends on claim 2. For examination purpose, the examiner has interpreted that the line 1 of claim 13 recites “ The system of claim 2, wherein the simulation module….”.
Claim 1 recites in line 6, “ from the group consisting of ..” .There is insufficient antecedent basis for this limitation in the claim. For examination purposes the examiner has interpreted “ from the group consisting of ..”, to be “ from a group consisting of ..”.
Claim 1 recites in line 11 and 13, “ the speaker” .There is insufficient antecedent basis for this limitation in the claim. For examination purposes the examiner has interpreted “ the speaker” of line 11, to be “ a speaker”.
Claim 7 recites in line 1, “ the transcript module” .There is insufficient antecedent basis for this limitation in the claim. For examination purposes the examiner has interpreted “ the transcript module” of line 1, to be “ a transcript module”.
Claim 13 recites in line 1, “ creates simulation translation” and in line 5 “ generate simulation translation”. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the examiner has interpreted, “ creates simulation translation” and “ generate simulation translation” to be “ creates a simulation translation” and “ generate the simulation translation” .
Claim 13 recites in line 4, “ the accuracy transcript”. There is insufficient antecedent basis for this limitation in the claim. For examination purposes the examiner has interpreted, “ the accuracy transcript” to be “ a accuracy transcript” .
Any claim not specifically treated is rejected by virtue of its dependency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11, 13-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Independent Claim 1 recites “A system of generating a real-time translation from a first language to a second language to maintain emotional fidelity from the first language to the second language, the system comprising: hardware and software such that the system comprises a sound sensor module configured to sense an audio signal”; “the system further comprising a sound processing module configured to convert the audio signal into an audio file”; “wherein the audio file comprises sounds selected from the group consisting of pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain”; “the system further comprising a storage module configured to store the audio file of the first language”; “the system further comprising a simulation module configured to generate a real-time simulation translation that matches the pitch, tone, bass, treble, emotional state, volume, language tense, speaking speed, and vocal strain of the speaker, wherein the real-time simulation translation further comprises an accurate transcript translating the first language of the speaker into the second language”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
A person with multi lingual capacity can listen (with ear which can be sound sensor module) to an utterance of a first language, from a recording or play or from another person and can realize ( with brain which can be sound processing module) the pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, vocal strain, related to the voice, can memorize or write down ( storage module) the utterance in first language and translate ( using brain which can be simulation module) to a second language with the same characteristics. The person can write down the translation of the first language to the second language in a paper. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “hardware”, “software”, nothing in the claim element precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer appliance does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
The claim recites the additional limitation of “sound sensor module”, “sound processing module”, “storage module”, “simulation module” for performing the method. All those are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 1 is therefore not drawn to eligible subject matter as this is directed to an abstract idea without significantly more than the abstract idea.
The Independent Claim 35 recites “A method of generating a real-time translation from a first language to a second language to maintain emotional fidelity from the first language to the second language, the method comprising: “a. acquiring and storing an audio file of the first language as it is spoken by a person”; “b. segregating the audio file into a plurality of distinct sections, wherein each of the plurality of distinct sections comprises data, said data comprising words and data relating to pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain”; “c. creating a transcript from each of the plurality of distinct sections”; “d. creating a first datafile with details relating to the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections”; “e. upon selection of a second language by a user, accessing a dataset relating to the second language, wherein the dataset comprises words”; “f. upon selection by the user of the second language, accessing a second datafile corresponding to pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain used in the second language”; “g. translating the transcript to the second language by matching the transcript with the dataset to create a translation transcript of the plurality of distinct sections”; “h. converting the first datafile into meta information by matching the information in the first datafile with information in the second datafile”; “i. combining the meta information with the translation transcript to match the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections”; “j. confirming the accuracy of the translation transcript”;” k. generating a real-time simulation matching the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain of the person”; “and I. sending the simulation to a device of a user”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
A person with multi lingual capacity can listen to an utterance of a first language, from another person and can realize the pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, vocal strain, related to the voice from the words, can memorize or write down the utterance in first language in distinct sections, can create a first data file with the details of voice characteristics, selecting a second language with the same characteristics by accessing datafile, translating the transcript to second language, generating a translation transcript, confirming the accuracy by matching the datafiles of transcript of first language with the data files of transcript of second language, generate the translation and record and can send to a user’s device. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The claim didn’t recite any other element which can precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer appliance does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
The claim doesn’t recite any additional elements for performing the method. The steps for performing the method are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using a generic computer component. Accordingly, the absence of additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Thus, taken alone, there is no elements which would amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 35 is therefore not drawn to eligible subject matter as this is directed to an abstract idea without significantly more than the abstract idea.
Claim 2 recites the additional limitation of “wherein the system further comprises a segregation module configured to separate the audio file into a plurality of distinct sections” , where separating the audio file could be performed in the human mind ( memorizing each section) or with the aid of pen and paper by marking each section in a paper to remember. The claim recites additional limitation of segregation module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 2 as drafted, is not patent eligible.
Claim 3 recites “wherein the segregation module is activated upon receiving an input from the storage module”, a person can decide which part of the audio he/she would like to separate, which could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitations of segregation module and storage module, which are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications, which are not sufficient to amount to significantly more than the judicial exception. The claim 3 as drafted, is not patent eligible.
Claim 4 recites “wherein the segregation module identifies individual elements of information to identify the plurality of distinct sections and further generates data relating to words, phrases, pitch, tone, emotional state, volume, language tense, speaking speed, vocal strain, bass, and treble”, to separate the data into different voice characteristics and to generate more related data, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of segregation module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 4 as drafted, is not patent eligible.
Claim 5 recites “wherein the segregation module identifies word information and sound information”, to separate the data into word and sound information, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of segregation module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 5 as drafted, is not patent eligible.
Claim 6 recites “wherein the system further comprises a transcription module configured to create a transcript 135e of the plurality of distinct sections and data”, to create a transcript of different sections of audio file, could be performed with the aid of pen and paper. The claim recites additional limitation of transcription module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 6 as drafted, is not patent eligible.
Claim 7 recites “wherein the transcript module is configured to create a first datafile that comprises information on the words and phrases spoken by the user as well as details relating to the pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain identified in each of the plurality of distinct sections”, while generating transcript, a datafile can be created with certain information such as words and phrases used by the user and the voice characteristics of the user, which could be performed with the aid of pen and paper. The claim recites additional limitation of transcript module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 7 as drafted, is not patent eligible.
Claim 8 recites “wherein the transcript module is configured to access a language list”, where determining and accessing a language list, could be an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of transcript module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 8 as drafted, is not patent eligible.
Claim 9 recites “wherein a user selects the second language from a memory”, where selecting of a language to translate, could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 9 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 10 recites “wherein the list is updated to ensure that the catalogue of languages is properly maintained.”, determining that the language list is updated is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 10 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 11 recites “wherein the list contains information relating to how words are used within the language”, to determine that the list has information on how words are used in a certain language, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 11 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 13 recites “The simulation module 155 creates simulation translation 155a by reconstructing the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections 121 into a uniform audio file 155b and layering the audio file 155b over the accuracy transcript 150a to generate simulation translation 155a”, where generating a translation by using the voice characteristics of the user, from different section of the transcript and aligning with accuracy transcript could be performed with the aid of pen and paper. The claim recites additional limitation of simulation module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 13 as drafted, is not patent eligible.
Claim 14 recites “wherein the transcription module accesses a second datafile stored in a memory that contains information corresponding to pitch, tone, emotional state, volume, speaking speed, and vocal strain that should be used in the second language”, to determine a second data file with information of voice characteristics in second language, is an evaluation, observation and could be performed with the aid of pen and paper. The claim recites additional limitation of transcription module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 14 as drafted, is not patent eligible.
Claim 15 recites “wherein the transcription module, upon identification of the appropriate emotional state after comparison of the second data file to a third data file selects the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation”, where the user can compare the appropriate emotional state in the voice characteristics of the user from a third data file, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of transcription module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 15 as drafted, is not patent eligible.
Claim 16 recites “wherein the transcription module sends a fourth data file comprising a transcript of the first language spoken by the speaker, information relating to the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation as well as the second language to be used to a translation module”, to determine a fourth data file with information of voice characteristics in first language and second language to be used for translation, is an evaluation, observation and could be performed with the aid of pen and paper. The claim recites additional limitation of transcription module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 16 as drafted, is not patent eligible.
Claim 17 recites “wherein the translation module is configured to receive the transcript from the transcription module and further configured to create a translation transcript from the transcript in view of the second language selected by the user”, translating according to the transcript could be performed in human mind or with the aid of pen and paper. The claim recites additional limitations of translation module and transcription module, which are recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which are not sufficient to amount to significantly more than the judicial exception. The claim 17 as drafted, is not patent eligible.
Claim 18 recites “further comprising a conversion module that is configured to receive the translation transcript and to receive information relating to the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation”, to determine how the translation will work based on the vocal characteristics of the user, could be performed with the aid of pen and paper. The claim recites additional limitation of conversion module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 18 as drafted, is not patent eligible.
Claim 19 recites “wherein the conversion module converts the information relating to the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation into meta information”, to determine how the translation will work based on the vocal characteristics of the user and converting the information in more details, could be performed with the aid of pen and paper. The claim recites additional limitation of conversion module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 19 as drafted, is not patent eligible.
Claim 20 recites “wherein the conversion module is configured to combine the meta information with the translation transcript to match the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections to create a conversion transcript”, to determine how the translation will work based on the vocal characteristics by combining the detail information of the voice characteristics of the user, could be performed with the aid of pen and paper. The claim recites additional limitation of conversion module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 20 as drafted, is not patent eligible.
Claim 21 recites “further comprising an accuracy module configured to scan the conversion transcript for errors and to correct said errors”, to check the accuracy of the transcript for any errors, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of accuracy module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 21 as drafted, is not patent eligible.
Claim 22 recites “wherein the accuracy module comprises a list of terms, including synonyms and antonyms, to be used within the second language for particular emotional contexts”, to determine the accuracy of the transcript consists of checking using synonyms and antonyms for particular emotions in second language, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recite additional limitation of accuracy module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 22 as drafted, is not patent eligible.
Claim 23 recites “wherein the accuracy module generates accuracy transcript”, to determine that the generated transcript is accurate, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of accuracy module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 23 as drafted, is not patent eligible.
Claim 24 recites “further comprises a simulation module configured to receive the accuracy transcript and generate a real-time simulation translation that matches the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain of the speaker”, to generate real time translation from an accurate transcript based on the vocal characteristics could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of simulation module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 24 as drafted, is not patent eligible.
Claim 25 recites “wherein the simulation module creates a simulation translation by reconstructing the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections into a uniform audio file and layering the audio file over the accuracy transcript to generate the simulation translation”, where generating a translation by using the voice characteristics of the user, from different section of the transcript and aligning with accuracy transcript could be performed with the aid of pen and paper. The claim recites additional limitation of simulation module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 25 as drafted, is not patent eligible.
Claim 26 recites “further comprises a device to receive and recite the simulation translation”, where the user can have a device to record , receive, recite translation such as a phone, which is an observation and could be performed in the human mind. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 26 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 27 and 36 recite “wherein the device is selected from the group consisting of a laptop computer, an earpiece, a headphone, a cell phone, a landline phone, a tablet, and an electronic device configured to play an audio file and receive audio file information”, where the user can have a device from a group of devices, to record , receive, recite translation, which is an observation and could be performed in the human mind. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 27 and 36 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claim 28 recites “further comprises a video module that receives a video data input 170a, wherein the video data input comprises information relating to video images being received by a video device ”, where the user can receive any images in his cell phone which is an observation and could be performed in the human mind. The claim recites additional limitation of video module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 28 as drafted, is not patent eligible.
Claim 29 recites “wherein the video device is a television, cell phone, tablet, computer, or other device capable of receiving and displaying videos”, where the user can have a device from a group of devices, to record , receive, displaying video, which is an observation and could be performed in the human mind. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 29 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 30 recites “wherein the video module, upon receiving video input, stores the video input in storage module”, where storing the video input such as images could be performed in the human mind ( memorizing each image) or with the aid of pen and paper by marking each image in a paper to remember. The claim recites additional limitation of video module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 30 as drafted, is not patent eligible.
Claim 31 recites “further comprises a video segregation module for segregating the video input into snapshot components”, where separating the images could be in a cell phone or could be performed in the human mind ( memorizing each section) or with the aid of pen and paper by marking each images in a paper to remember. The claim recites additional limitation of video segregation module, which is recited at a high level of generality and is recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 31 as drafted, is not patent eligible.
Claim 32 recites “wherein the video segregation module transmits the snapshot components to video compilation module”, where the user can send the images to his cell phone or memorize or can put the images in a paper. Al those could be an observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitations of video segregation module and video compilation module, which are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications, which is not sufficient to amount to significantly more than the judicial exception. The claim 32 as drafted, is not patent eligible.
Claim 33 recites “further comprises a video compilation module communicates with simulation module to receive simulation translation”, where the user can translate based on the images which is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of video compilation module and simulation module , which are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications, which are not sufficient to amount to significantly more than the judicial exception. The claim 33 as drafted, is not patent eligible.
Claim 34 recites “further comprises simulation translation to create a video simulation translation”, where the user can translate based on images, which is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 34 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10, 13-17, 25 and 28-34 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. ( US 20220044668 A1), hereinafter referenced as Kumar, in view of de Juan et al. (US 12,505,859 B2), hereinafter referenced as de Juan, further in view of Gupta et al. (US 20220358905 A1), hereinafter referenced as Gupta.
Regarding Claim 1, Kumar teaches a system of generating a real-time translation from a first language to a second language to maintain emotional fidelity from the first language to the second language ( Kumar: Para.[0004],[0021],[0091], Fig. 1, illustrates a scenario 100 where a user is watching a broadcast in a media system installed in user device 114, which can translates the audio from English to Spanish in real time. When synthesizing speech for the translated audio, the media system adds the emotion to the translated words), the system comprising:
the system further comprising a sound processing module configured to convert the audio signal into an audio file ( Kumar: Para.[0031], [0036],Fig. 2 illustrates media devices 200 and 201, both system includes control circuitry 204 which include audio circuitry, video circuitry, and tuning circuitry, such as one or more analog tuners, one or more MP4 decoders or other digital decoding circuitry, or any other suitable tuning or audio circuits or combinations of such circuits. Encoding circuitry converts analog, or digital signals to audio signals for storage. Para.[0023], The media system stores a portion ( as audio file) of the received broadcast in a buffer );
wherein the audio file comprises sounds selected from the group consisting of pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, [and vocal strain] ( Kumar: Para.[0023], [0025]-[0027], Fig. 1, The media system stores a portion ( audio file) of the received broadcast of the "Oscars" in a buffer. The portion of the "Oscars" stored in the buffer may contain a part of the acceptance speech "I wish to dedicate this award to my family," which contains emotional state, volume, pitch, timbre, tone, accent, treble ( high pitch) and rhythm ( speaking speed). The media system translates by analyzing grammar rules for both language ( language tense). Non-linguistic vocal characteristics could be gender based; such male voices could be grouped in bass);
the system further comprising a storage module configured to store the audio file of the first language ( Kumar: Para.[0047], Fig. 4, at 404 The control circuitry 204 may periodically receive a portion of the media asset from media source. The received portion ( audio file) of the media asset may be stored in a buffer in storage 208);
the system further [comprising a simulation module] configured to generate a real-time simulation translation that matches the pitch, tone, bass, treble, emotional state, volume, language tense, speaking speed, and [vocal strain] of the speaker ( Kumar: Para.[0080],Fig. 8, at 820, control circuitry 204 translates speech using selected non-linguistic characteristics. Because the spoken words are being translated to Spanish, the media system may synthesize the speech using the non-linguistic characteristics of a Spanish person. Para.[0025]-[0027], emotion states may be determined by non-linguistic characteristics such as, volume, pitch, timbre, tone, accent, treble ( high pitch) and rhythm ( speaking speed). The media system translates by analyzing grammar rules for both language ( language tense). Non-linguistic vocal characteristics could be gender based; such male voices could be grouped in bass);
Kumar while teaching the system of claim 1, fails to explicitly teach the claimed, hardware and software such that the system comprises a sound sensor module configured to sense an audio signal; wherein the audio file comprises sounds selected from the group consisting of [pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and] vocal strain; the system further comprising a simulation module configured to generate a real-time simulation translation that matches the pitch, tone, bass, treble, emotional state, volume, language tense, speaking speed, and vocal strain of the speaker, wherein the real-time simulation translation further comprises an accurate transcript translating the first language of the speaker into the second language.
However, de Juan does teach the claimed, hardware and software such that the system comprises a sound sensor module configured to sense an audio signal ( de Juan: Column 5, lines 27-36, the client device can have a sound adapter coupled with a speaker to receive the input from a user such as microphone);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate de Juan’s teaching of devices, systems, and/or methods for generating a video in a target language, into the system and method of translating the audio from a first language to a second language for a media stream , taught by Kumar, because, by generating video in second language by matching the speaker profile can improve user experience. (de Juan, Column 6, lines 56-67, column 7, lines 1-20).
Kumar in view of de Juan while teaching the system of claim 1, fail to explicitly teach the claimed, wherein the audio file comprises sounds selected from the group consisting of [pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and] vocal strain; the system further comprising a simulation module configured to generate a real-time simulation translation that matches the pitch, tone, bass, treble, emotional state, volume, language tense, speaking speed, and vocal strain of the speaker, wherein the real-time simulation translation further comprises an accurate transcript translating the first language of the speaker into the second language.
However, Gupta does teach the claimed, wherein the audio file comprises sounds selected from the group consisting of [pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and] vocal strain ( Gupta: Para.[0065], Fig.4, Meta information processor 130 may be configured to identify and associate various meta information with each vocal segment, such as emotion, stress ( vocal strain), pacing/prosody/rhythm, phoneme analysis, tone, age, gender, race);
the system further comprising a simulation module configured to generate a real-time simulation translation that matches the pitch, tone, bass, treble, emotional state, volume, language tense, speaking speed, and vocal strain of the speaker ( Gupta: Para.[0077],[0084], Figs. 2, 6, at step 210, audio translation generator 138 translate first language 110 to second language 112, from the translated transcription 134. The resulting translation 140 matches spoken words, emotion, pacing, pauses, tone, prosody, intensity/ tone, stress, vocal identity. Para.[0186], the translation can be in real time),
wherein the real-time simulation translation further comprises an accurate transcript translating the first language of the speaker into the second language ( Gupta: Para.[0073],[0074],Fig.5,based on the input transcription of first language 110 and the second language 112, input meta information 131 ( which improves the transcription), transcription and meta translation generator 132 produce translated transcription 134. Para.[0185], Fig.13 illustrates and implementation where an accurate transcription and translation can be achieved by using trained GAN network).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 2, Kumar in view of de Juan, further in view of Gupta teach the system of claim 1. Kumar further teaches, wherein the system further comprises a segregation module configured to separate the audio file into a plurality of distinct sections ( Kumar: Para.[0023], the media system stores the received broadcast in portions associated with metadata, in a buffer).
Regarding Claim 3, Kumar in view of de Juan, further in view of Gupta teach the system of claim 2. Kumar further teaches, wherein the segregation module is activated upon receiving an input from the storage module ( Kumar: Para.[0004],[0046], Fig. 3, user send a requests to access a media asset, (e.g., "Oscars") on media device 302. The media system buffers a portion of the stream of the "Oscars", and extracts the audio of the portion of the stream from the buffer).
Regarding Claim 4, Kumar in view of de Juan, further in view of Gupta teach the system of claim 2. Kumar further teaches, wherein the segregation module identifies individual elements of information to identify the plurality of distinct sections and further generates data relating to [words, phrases,] pitch, tone, emotional state, volume, language tense, speaking speed, [vocal strain], bass, and treble ( Kumar: Para.[0023]-[0027], Fig. 1, The media system stores a portion ( audio file) of the received broadcast of the "Oscars" in a buffer. The portion of the "Oscars" stored in the buffer may contain a part of the acceptance speech "I wish to dedicate this award to my family," which contains emotional state, volume, pitch, timbre, tone, accent, treble ( high pitch) and rhythm ( speaking speed). The media system translates by analyzing grammar rules for both language ( language tense). Non-linguistic vocal characteristics could be gender based; such male voices could be grouped in bass);
Gupta further teaches, wherein the segregation module identifies individual elements of information to identify the plurality of distinct sections and further generates data relating to words, phrases, [pitch, tone, emotional state, volume, language tense, speaking speed, ] vocal strain, [bass, and treble] ( Gupta: Para.[0065], Fig.4, Meta information processor 130 may be configured to identify and associate various meta information with each vocal segment, such as emotion, stress ( vocal strain), pacing/prosody/rhythm, phoneme analysis, tone, age, gender, race);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 5, Kumar in view of de Juan, further in view of Gupta teach the system of claim 2. Kumar further teaches, wherein the segregation module identifies word information and sound information ( Kumar: Para. [0023], [0025], the spoken words of the audio component and the non-linguistic characteristics such as volume, pitch, timbre, tone, accent, and rhythm ( sound information) can be identified),
Regarding Claim 6, Kumar in view of de Juan, further in view of Gupta teach the system of claim 1. Gupta further teaches, wherein the system further comprises a transcription module configured to create a transcript 135e of the plurality of distinct sections and data ( Gupta: Para.[0073], [0074], Fig.5, transcription and meta translation generator 132 generate translated transcription 134, based on the input transcription of first language 110 and the second language 112, input meta information 131).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 7, Kumar in view of de Juan, further in view of Gupta teach the system of claim 5. Gupta further teaches, wherein the transcript module is configured to create a first datafile that comprises information on the words and phrases spoken by the user as well as details relating to the pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain identified in each of the plurality of distinct sections ( Gupta: Para.[0059],[0060], Fig. 3, input transcription generator 127 convert the vocal segments of the speaker into segmented input transcriptions 108 ( first data file), which includes words spoken to highly detailed data about mouth movements, phonemes, language(s) being spoken, identification of names/proper nouns, sentiment analysis, time stamps/time indices of words and/or syllables, and/or phonemes with timestamps for each separate person speaking in the audio).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 8, Kumar in view of de Juan, further in view of Gupta teach the system of claim 5. Gupta further teaches, wherein the transcript module is configured to access a language list ( Gupta: Para.[0075], transcription and meta translation generator 132 is provided with multiple language options such as English to German and Spanish to German or English to German and Spanish to French).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 9, Kumar in view of de Juan, further in view of Gupta teach the system of claim 8. Kumar further teaches, wherein a user selects the second language from a memory ( Kumar: Para.[0022], Fig. 1, user can select the preferred language from the language menu 106, stored in the media system, via an interface of the media system).
Regarding Claim 10, Kumar in view of de Juan, further in view of Gupta teach the system of claim 8. Kumar further teaches, wherein the list is updated to ensure that the catalogue of languages is properly maintained ( Kumar: Para.[0022], media system is able to provide audio in many more languages that are not displayed in "Language" menu 106).
Regarding Claim 13, Kumar in view of de Juan, further in view of Gupta teach the system of claim 2. Gupta further teaches, the simulation module 155 creates simulation translation 155a by reconstructing the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections 121 into a uniform audio file 155b and layering the audio file 155b over the accuracy transcript 150a to generate simulation translation 155a ( Gupta: Para.[0073],[0074],Fig.5, based on the input transcription of first language 110 and the second language 112, input meta information 131 ( which improves the transcription), transcription and meta translation generator 132 produce translated transcription 134. Para.[0185], Fig.13 illustrates and implementation where an accurate transcription and translation can be achieved by using trained GAN network. Para.[0077],[0084], Figs. 2, 6, at step 210, audio translation generator 138 translate first language 110 to second language 112, from the translated transcription 134. The resulting translation 140 matches spoken words, emotion, pacing, pauses, tone, prosody, intensity/ tone, stress, vocal identity),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 14, Kumar in view of de Juan, further in view of Gupta teach the system of claim 5. Kumar further teaches, wherein the transcription module accesses a second datafile stored in a memory that contains information corresponding to pitch, tone, emotional state, volume, speaking speed, and vocal strain that should be used in the second language ( Kumar: Para.[0062], Fig. 6, at 606, control circuitry 204 retrieves, from the language translation database, rules of grammar rules of the second language. Para.[0086], vocal fingerprint is retrieved to accurately synthesize speech in second language).
Regarding Claim 15, Kumar in view of de Juan, further in view of Gupta teach the system of claim 5. Kumar further teaches, wherein the transcription module, upon identification of the appropriate emotional state after comparison of the second data file to a third data file selects the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation ( Kumar: Para.[0067], Fig.7, at 704, At 704, control circuitry 204 compares the emotional identifier to a plurality of emotional identifiers stored in a database ( third file). Para.[0072], Fig. 8, at 806 control circuitry 204 determines whether the non-linguistic characteristics match an emotional state. In response to determining that the nonlinguistic characteristics match an emotional state, control circuitry 204 proceeds process to synthesize speech using the emotional state).
Regarding Claim 16, Kumar in view of de Juan, further in view of Gupta teach the system of claim 5. Gupta further teaches, wherein the transcription module sends a fourth data file comprising a transcript of the first language spoken by the speaker, information relating to the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation as well as the second language to be used to a translation module ( Gupta: Para.[0069], [0072]-[0074], Figs.2, 5, At step 210, input transcription 108 is translated from input language 110 to output language 112. Various inputs are provided to transcription and meta translation generator 132, inputs include input transcription 108, outputs from text preprocessor 128, and input meta information 131 and output language 112. Meta information 131 includes speech characteristics).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 17, Kumar in view of de Juan, further in view of Gupta teach the system of claim 16. Gupta further teaches, wherein the translation module is configured to receive the transcript from the transcription module and further configured to create a translation transcript from the transcript in view of the second language selected by the user ( Gupta: Para.[0073], [0074], Fig.5, based on the input transcription of first language 110 and the second language 112, input meta information 131, transcription and meta translation generator 132 produce translated transcription 134).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 28, Kumar in view of de Juan, further in view of Gupta teach the system of claim 1. Gupta further teaches, further comprises a video module that receives a video data input 170a, wherein the video data input comprises information relating to video images being received by a video device ( Gupta: Para.[0052],[0100], Fig. 3, video input 104 is received by the video preprocessor 124 where video preprocessor 124 is configured to identify and track lip movements, which are used to determine which speaker is speaking during a particular vocal segment in the video).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 29, Kumar in view of de Juan, further in view of Gupta teach the system of claim 28. Kumar further teaches, wherein the video device is a television, cell phone, tablet, computer, or other device capable of receiving and displaying videos ( Kumar: Para.[0034], the media device could be a television, a Smart TV, a streaming media device, a personal computer (PC), a laptop computer, a tablet, a WebTV box, a smart phone, or any other television equipment, computing equipment, or wireless device, and/or combination of the same).
Regarding Claim 30, Kumar in view of de Juan, further in view of Gupta teach the system of claim 28. Gupta further teaches, wherein the video module, upon receiving video input, stores the video input in storage module ( Gupta: Para.[0177], video preprocessor 124 runs facial recognition and alignment of faces in the input video. This may be done in cloud or on device).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 31, Kumar in view of de Juan, further in view of Gupta teach the system of claim 28. Gupta further teaches, further comprises a video segregation module for segregating the video input into snapshot components ( Gupta: Para.[0051],[0057], Video preprocessor 124 may include processes for identifying and tracking subjects within the video. For example, video preprocessor 124 may employ facial detection software that track each subject depicted in the video. The data associated with facial trajectory analysis may include the start and end time during which the face is depicted, individual subject identities compared to others, gender, time on screen, time of speaking based on audio, and lip sync analysis to identify who is talking).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 32, Kumar in view of de Juan, further in view of Gupta teach the system of claim 31. Gupta further teaches, wherein the video segregation module transmits the snapshot components to video compilation module ( Gupta: Para.[0087],Fig. 7, outputs from video preprocessor 124 are provided to video sync generator 144 ( video compilation module)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 33, Kumar in view of de Juan, further in view of Gupta teach the system of claim 31. Gupta further teaches, further comprises a video compilation module communicates with simulation module to receive simulation translation ( Gupta: Para.[0087], Fig. 7, translated audio 140 from audio translation generator 138 is provided to video sync generator 144).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 34, Kumar in view of de Juan, further in view of Gupta teach the system of claim 33. Gupta further teaches, further comprises simulation translation to create a video simulation translation ( Gupta: Para.[0089], Fig. 7, Using the provided information, video sync generator 144 produces synced video 146 in which translated audio 140 is dubbed over input video 104 and the speaker's facial movements coincide with translated audio 140).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Claims 11, 18-27 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. ( US 20220044668 A1), hereinafter referenced as Kumar, in view of de Juan et al. (US 12,505,859 B2), hereinafter referenced as de Juan, further in view of Gupta et al. (US 20220358905 A1), hereinafter referenced as Gupta, further in view of Ghatage et al. (US 20230325612 A1), hereinafter referenced as Ghatage.
Regarding Claim 11, Kumar in view of de Juan, further in view of Gupta teach the system of claim 8. Kumar in view of de Juan, further in view of Gupta fail to explicitly teach the claimed, wherein the list contains information relating to how words are used within the language.
However, Ghatage does teach the claimed, wherein the list contains information relating to how words are used within the language ( Ghatage: Para.[0035], Fig.3, the plurality of domain glossaries 350 for various domains in different languages may be used so that when the language detector 322 identifies the source language, a subset of the plurality of domain glossaries 350 of the source language is selected for domain identification by the ML domain classification model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghatage’s teaching of system and method for multi-platform voice analysis and translation, into the system and method, taught by Kumar in view of de Juan, further in view of Gupta, because, by using an end-to-end AI-driven natural language processing (NLP) platform that enables translation of voice communications for a plurality of platforms in a plurality of languages by employing the plurality of translation engines and by using speaker diarization, the system improves the audio quality provided to the translation engines thereby increasing the accuracy of the translation output. (Ghatage, Para.[0021]).
Regarding Claim 18, Kumar in view of de Juan, further in view of Gupta teach the system of claim 17. Kumar in view of de Juan, further in view of Gupta fail to explicitly teach the claimed, further comprising a conversion module that is configured to receive the translation transcript and to receive information relating to the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation.
However, Ghatage does teach the claimed, further comprising a conversion module that is configured to receive the translation transcript and to receive information relating to the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation ( Ghatage: Para.[0050],Fig. 10, intermediate summary of the communication session ( post translation transcript) , generated at 1008 and sentiment information at 1010, are received by the transcription engine 180).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghatage’s teaching of system and method for multi-platform voice analysis and translation, into the system and method, taught by Kumar in view of de Juan, further in view of Gupta, because, by using an end-to-end AI-driven natural language processing (NLP) platform that enables translation of voice communications for a plurality of platforms in a plurality of languages by employing the plurality of translation engines and by using speaker diarization, the system improves the audio quality provided to the translation engines thereby increasing the accuracy of the translation output. (Ghatage, Para.[0021]).
Regarding Claim 19, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 18. Ghatage further teaches, wherein the conversion module converts the information relating to the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, treble, bass, and vocal strain to be used for a simulation into meta information ( Ghatage: Para.[0050],Fig. 10, at 1012, the sentiment information made available by the sentiment analyzer 306 as a stream is accessed. The timeline of the summary obtained at 1008 is matched with the sentiment stream ( meta information)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghatage’s teaching of system and method for multi-platform voice analysis and translation, into the system and method, taught by Kumar in view of de Juan, further in view of Gupta, because, by using an end-to-end AI-driven natural language processing (NLP) platform that enables translation of voice communications for a plurality of platforms in a plurality of languages by employing the plurality of translation engines and by using speaker diarization, the system improves the audio quality provided to the translation engines thereby increasing the accuracy of the translation output. (Ghatage, Para.[0021]).
Regarding Claim 20, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 18. Ghatage further teaches, wherein the conversion module is configured to combine the meta information with the translation transcript to match the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections to create a conversion transcript ( Ghatage: Para.[0050],Fig. 10, The final summary including the summary ( transcript) of the conversations, sentiments, and details of the speakers can be generated at 1014).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghatage’s teaching of system and method for multi-platform voice analysis and translation, into the system and method, taught by Kumar in view of de Juan, further in view of Gupta, because, by using an end-to-end AI-driven natural language processing (NLP) platform that enables translation of voice communications for a plurality of platforms in a plurality of languages by employing the plurality of translation engines and by using speaker diarization, the system improves the audio quality provided to the translation engines thereby increasing the accuracy of the translation output. (Ghatage, Para.[0021]).
Regarding Claim 21, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 20. Gupta further teaches, further comprising an accuracy module configured to scan the conversion transcript for errors and to correct said errors ( Gupta: Para.[0185], Fig. 13, trained custom GAN network can be used for improved quality and accuracy for transcription).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan further in view of Ghatage, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 22, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 21. Ghatge further teaches, wherein the accuracy module comprises a list of terms, including synonyms and antonyms, to be used within the second language for particular emotional contexts ( Ghatage: Para.[0049], Pre-trained advanced natural language processing (NLP) models can be used by the sentiment analyzer for text transformation. These models provide greater accuracy and speed if trained for specific languages. Hence based on the output translated language, the NLP model can be chosen and which can be derived from the text2text generation models from the transformers library. The NLP model can be tuned using custom datasets and a tokenizer. The NLP model will need to be fine-tuned for each language using language-specific data sets. Based on the sentiment of the speaker, the NLP model can generate sentences that are snore suitable for the speaker's sentiment or emotion).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghatage’s teaching of system and method for multi-platform voice analysis and translation, into the system and method, taught by Kumar in view of de Juan further in view of Gupta, because, by using an end-to-end AI-driven natural language processing (NLP) platform that enables translation of voice communications for a plurality of platforms in a plurality of languages by employing the plurality of translation engines and by using speaker diarization, the system improves the audio quality provided to the translation engines thereby increasing the accuracy of the translation output. (Ghatage, Para.[0021]).
Regarding Claim 23, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 21. Gupta further teaches, wherein the accuracy module generates accuracy transcript ( Gupta: Para.[0185], Fig. 13, trained custom GAN network can generate transcript with improved quality and accuracy).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan further in view of Ghatage, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 24, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 23. Gupta further teaches, further comprises a simulation module configured to receive the accuracy transcript and generate a real-time simulation translation that matches the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain of the speaker ( Gupta: Para.[0077],[0084], Figs. 2, 6, at step 210, audio translation generator 138 translate first language 110 to second language 112, from the translated transcription 134. The resulting translation 140 matches spoken words, emotion, pacing, pauses, tone, prosody, intensity/ tone, stress, vocal identity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan further in view of Ghatage, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 25, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 24. Gupta further teaches, wherein the simulation module creates a simulation translation by reconstructing the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections into a uniform audio file and layering the audio file over the accuracy transcript to generate the simulation translation ( Gupta: Para.[0073],[0074],Fig.5, based on the input transcription of first language 110 and the second language 112, input meta information 131 ( which improves the transcription), transcription and meta translation generator 132 produce translated transcription 134. Para.[0185], Fig.13 illustrates and implementation where an accurate transcription and translation can be achieved by using trained GAN network. Para.[0077],[0084], Figs. 2, 6, at step 210, audio translation generator 138 translate first language 110 to second language 112, from the translated transcription 134. The resulting translation 140 matches spoken words, emotion, pacing, pauses, tone, prosody, intensity/ tone, stress, vocal identity),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method, taught by Kumar in view of de Juan, further in view of Ghatage, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Regarding Claim 26, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 25. Kumar further teaches, further comprises a device to receive and recite the simulation translation ( Kumar: Para.[0022],[0031], Figs. 1, 2, the user watches “Oscars” which is broadcasted in English and user is listening in Spanish, in a user device 114, which can be smartphone or tablet or home media system).
Regarding Claim 27, Kumar in view of de Juan, further in view of Gupta, further in view of Ghatage teach the system of claim 26. Kumar further teaches, wherein the device is selected from the group consisting of a laptop computer, [an earpiece, a headphone], a cell phone, a landline phone, a tablet, and an electronic device configured to play an audio file and receive audio file information ( Kumar: Para.[0034], the media device could be a streaming media device, a laptop computer, a tablet, a smart phone, or any other television equipment, computing equipment, or wireless device, and/or combination of the same).
de Juan further teaches, wherein the device is selected from the group consisting of [a laptop computer,] an earpiece, a headphone,[ a cell phone, a landline phone, a tablet, and an electronic device configured] to play an audio file and receive audio file information ( de Juan: Column 5, lines 4-10, the client device could be an earpiece, a wearable device mountable in a headset).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate de Juan’s teaching of devices, systems, and/or methods for generating a video in a target language, into the system and method of translating the audio from a first language to a second language for a media stream , taught by Kumar in view of Gupta, further in view of Ghatage, because, by generating video in second language by matching the speaker profile can improve user experience. (de Juan, Column 6, lines 56-67, column 7, lines 1-20).
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. ( US 20220044668 A1), hereinafter referenced as Kumar, in view of Gupta et al. (US 20220358905 A1), hereinafter referenced as Gupta, further in view of Ghatage et al. (US 20230325612 A1), hereinafter referenced as Ghatage.
Regarding Claim 35, Kumar teaches a method of generating a real-time translation from a first language to a second language to maintain emotional fidelity from the first language to the second language ( Kumar: Para.[0004],[0021],[0091], Fig. 1, illustrates a scenario 100 where a user is watching a broadcast in a media system installed in user device 114, which can translates the audio from English to Spanish in real time. When synthesizing speech for the translated audio, the media system adds the emotion to the translated words), the method comprising:
a. acquiring and storing an audio file of the first language as it is spoken by a person ( Kumar: Para.[0031], [0036],Fig. 2 illustrates media devices 200 and 201, both system includes control circuitry 204 which include audio circuitry, video circuitry, and tuning circuitry, such as one or more analog tuners, one or more MP4 decoders or other digital decoding circuitry, or any other suitable tuning or audio circuits or combinations of such circuits. Encoding circuitry converts analog, or digital signals to audio signals for storage. Para.[0023], The media system stores a portion ( as audio file) of the received broadcast in first language in a buffer );
b. segregating the audio file into a plurality of distinct sections, wherein each of the plurality of distinct sections comprises data, said data comprising words and data relating to pitch, tone, emotional state, volume, language tense, speaking speed, [and vocal strain] ( Kumar: Para.[0023]-[0027], Fig.1, The media system stores a portion ( audio file) of the received broadcast of the "Oscars" in a buffer. The portion of the "Oscars" stored in the buffer may contain a part of the acceptance speech "I wish to dedicate this award to my family," which contains emotional state, volume, pitch, timbre, tone, accent, treble ( high pitch) and rhythm ( speaking speed). The media system translates by analyzing grammar rules for both language ( language tense). Non-linguistic vocal characteristics could be gender based; such male voices could be grouped in bass).
f. upon selection by the user of the second language, accessing a second datafile corresponding to pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain used in the second language ( Kumar: Para.[0062], Fig. 6, at 606, control circuitry 204 retrieves, from the language translation database, rules of grammar rules of the second language. Para.[0086], vocal fingerprint is retrieved to accurately synthesize speech in second language);
I. sending the simulation to a device of a user ( Kumar: Para.[0022],[0031], Figs. 1, 2, the user watches “Oscars” which is broadcasted in English and user is listening in Spanish, in a user device 114, which can be smartphone or tablet or home media system).
Kumar while teaching the method of claim 35, fails to explicitly teach the claimed, b. segregating the audio file into a plurality of distinct sections, wherein each of the plurality of distinct sections comprises data, said data comprising words [and data relating to pitch, tone, emotional state, volume, language tense, speaking speed,] and vocal strain; c. creating a transcript from each of the plurality of distinct sections; d. creating a first datafile with details relating to the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections; e. upon selection of a second language by a user, accessing a dataset relating to the second language, wherein the dataset comprises words; g. translating the transcript to the second language by matching the transcript with the dataset to create a translation transcript of the plurality of distinct sections; h. converting the first datafile into meta information by matching the information in the first datafile with information in the second datafile; i. combining the meta information with the translation transcript to match the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections; j. confirming the accuracy of the translation transcript; k. generating a real-time simulation matching the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain of the person;
However, Gupta does teach the claimed, b. segregating the audio file into a plurality of distinct sections, wherein each of the plurality of distinct sections comprises data, said data comprising words [and data relating to pitch, tone, emotional state, volume, language tense, speaking speed,] and vocal strain ( Gupta: Para.[0065], Fig.4, Meta information processor 130 may be configured to identify and associate various meta information with each vocal segment, such as emotion, stress ( vocal strain), pacing/prosody/rhythm, phoneme analysis, tone, age, gender, race);
c. creating a transcript from each of the plurality of distinct sections ( Gupta: Para.[0073], [0074], Fig.5, transcription and meta translation generator 132 generate translated transcription 134, based on the input transcription of first language 110 and the second language 112, input meta information 131);
d. creating a first datafile with details relating to the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections ( Gupta: Para.[0059],[0060], Fig. 3, input transcription generator 127 convert the vocal segments of the speaker into segmented input transcriptions 108 ( first data file), which includes words spoken to highly detailed data about mouth movements, phonemes, language(s) being spoken, identification of names/proper nouns, sentiment analysis, time stamps/time indices of words and/or syllables, and/or phonemes with timestamps for each separate person speaking in the audio);
g. translating the transcript to the second language by matching the transcript with the dataset to create a translation transcript of the plurality of distinct sections ( Gupta: Para.[0073], [0074], Fig.5, transcription and meta translation generator 132 generate translated transcription 134, based on the input transcription of first language 110 and the second language 112, input meta information 131);
h. converting the first datafile into meta information by matching the information in the first datafile with information in the second datafile ( Gupta: Para.[0069], [0072]-[0074], Figs.2, 5, At step 210, input transcription 108 is translated from input language 110 to output language 112. Various inputs are provided to transcription and meta translation generator 132, inputs include input transcription 108, outputs from text preprocessor 128, and input meta information 131 and output language 112. Meta information 131 includes speech characteristics);
j. confirming the accuracy of the translation transcript ( Gupta: Para.[0185], Fig. 13, trained custom GAN network can generate transcript with improved quality and accuracy);
k. generating a real-time simulation matching the pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain of the person ( Gupta: Para.[0073],[0074],Fig.5, based on the input transcription of first language 110 and the second language 112, input meta information 131 ( which improves the transcription), transcription and meta translation generator 132 produce translated transcription 134. Para.[0185], Fig.13 illustrates and implementation where an accurate transcription and translation can be achieved by using trained GAN network. Para.[0077],[0084], Figs. 2, 6, at step 210, audio translation generator 138 translate first language 110 to second language 112, from the translated transcription 134. The resulting translation 140 matches spoken words, emotion, pacing, pauses, tone, prosody, intensity/ tone, stress, vocal identity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Gupta’s teaching of system and method for translating audio, and video when desired by using synthetic media and data generated using AI systems, into the system and method of translating the audio from a first language to a second language for a media stream, taught by Kumar, because, by using AI generator and meta information including emotions and tone data, the transcription and translation from one language to another language would be improved.(Gupta, Para.[0013]-[0015]).
Kumar in view of Gupta, while teaching the method of claim 35, fail to explicitly teach the claimed, e. upon selection of a second language by a user, accessing a dataset relating to the second language, wherein the dataset comprises words; i. combining the meta information with the translation transcript to match the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections.
However, Ghatage does teach the claimed, e. upon selection of a second language by a user, accessing a dataset relating to the second language, wherein the dataset comprises words ( Ghatage: Para.[0035], Fig.3, the plurality of domain glossaries 350 for various domains in different languages may be used so that when the language detector 322 identifies the source language, a subset of the plurality of domain glossaries 350 of the source language is selected for domain identification by the ML domain classification model);
i. combining the meta information with the translation transcript to match the appropriate pitch, tone, emotional state, volume, language tense, speaking speed, and vocal strain identified in each of the plurality of distinct sections ( Ghatage: Para.[0050],Fig. 10, The final summary including the summary ( transcript) of the conversations, sentiments, and details of the speakers can be generated at 1014).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ghatage’s teaching of system and method for multi-platform voice analysis and translation, into the system and method, taught by Kumar in view of Gupta, because, by using an end-to-end AI-driven natural language processing (NLP) platform that enables translation of voice communications for a plurality of platforms in a plurality of languages by employing the plurality of translation engines and by using speaker diarization, the system improves the audio quality provided to the translation engines thereby increasing the accuracy of the translation output. (Ghatage, Para.[0021]).
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. ( US 20220044668 A1), hereinafter referenced as Kumar, in view of Gupta et al. (US 20220358905 A1), hereinafter referenced as Gupta, further in view of Ghatage et al. (US 20230325612 A1), hereinafter referenced as Ghatage, further in view of de Juan et al. (US 12,505,859 B2), hereinafter referenced as de Juan.
Regarding Claim 36, Kumar in view of Gupta, further in view of Ghatage teach the method of claim 35. Kumar further teaches, wherein the device is selected from the group consisting of a laptop computer, [an earpiece, a headphone], a cell phone, a landline phone, a tablet, and an electronic device configured to play an audio file and receive audio file information ( Kumar: Para.[0034], the media device could be a streaming media device, a laptop computer, a tablet, a smart phone, or any other television equipment, computing equipment, or wireless device, and/or combination of the same).
Kumar in view of Gupta, further in view of Ghatage while teaching the method of claim 36, fail to explicitly teach, wherein the device is selected from the group consisting of [a laptop computer,] an earpiece, a headphone,[ a cell phone, a landline phone, a tablet, and an electronic device configured] to play an audio file and receive audio file information.
However, de Juan further teaches, wherein the device is selected from the group consisting of [a laptop computer,] an earpiece, a headphone,[ a cell phone, a landline phone, a tablet, and an electronic device configured] to play an audio file and receive audio file information ( de Juan: Column 5, lines 4-10, the client device could be an earpiece, a wearable device mountable in a headset).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate de Juan’s teaching of devices, systems, and/or methods for generating a video in a target language, into the system and method of translating the audio from a first language to a second language for a media stream , taught by Kumar in view of Gupta, further in view of Ghatage, because, by generating video in second language by matching the speaker profile can improve user experience. (de Juan, Column 6, lines 56-67, column 7, lines 1-20).
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant's disclosure.
Rathnam et al. (US 20220051656 A1) teaches a system for using cloud structures in real time speech and translation involving multiple languages is provided. The system comprises a processor, a memory, and an application stored in the memory that when executed on the processor receives audio content in a first spoken language from a first speaking device. The system also receives a first language preference from a first client device, the first language preference differing from the spoken language. The system also receives a second language preference from a second client device, the second language preference differing from the spoken language. The system also transmits the audio content and the language preferences to at least one translation engine and receives the audio content from the engine translated into the first and second languages. The system also sends the audio content to the client devices translated into their respective preferred languages.
Moy et al. (US 20230096543 A1) teaches Systems and methods for providing one-to-one and audio and video calls or for providing multi-party audio or video conferences also provide language translation services. When language translation services are provided, a party to a call or conference hears both the audio of the speaker, and a translated version of the speaker's audio.
Ingel et al. (US 20210224319 A1) teaches methods and systems for artificially generating media streams. Textual information, rhythm information and voice characteristics may be received. It may be determined that a first portion of the textual information corresponds to a first portion of the rhythm information and that a second portion of the textual information corresponds to a second portion of the rhythm information. Audio stream may be generated based on the textual information, the rhythm information and the voice characteristics. A first portion of the audio stream may include a vocal expression of the first portion of the textual information in a voice corresponding to the voice characteristics and according to the first portion of the rhythm information, and a second portion may include a vocal expression of the second portion of the textual information in the voice corresponding to the voice characteristics and according to the second portion of the rhythm information.
Lin et al. (US 20190028797 A1 ) teaches a high-fidelity audio device including a main microphone, a voice microphone and a process circuit. The main microphone receives sound and generates a main signal; the voice microphone perceives user's vocal vibration and produces a vocal signal; the process circuit collects the main signal and the vocal signal, superimposes and then decays the collected signals to generate a high-fidelity signal for high-definition broadcast realization.
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/NADIRA SULTANA/Examiner, Art Unit 2653